Broker-Insights: An Interactive and Visual Recommendation System for Insurance Brokerage

  • Paul Dany AtauchiEmail author
  • Luciana Nedel
  • Renata Galante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)


The black box nature of the recommendation systems limits the understanding and acceptance of the recommendation received by the user. In contrast, user interaction and information visualization play a key role in addressing these drawbacks. In the brokerage domain, insurance brokers offer, negotiate and sell insurance products for their customers. Support brokers into the recommendation process can improve the loyalty, profit, and marketing campaign in their client portfolio. This work presents Broker-Insights, an interactive and visualisation-based insurance products recommender system to support brokers into the decision-making (recommendation) at two levels: recommendations for a specific potential customer; and recommendations for a group of customers. Looking for offering personalized recommendations, Broker-Insights provides a tool to manage customers information in the recommendation task and a module to perform customers segmentation based on specific characteristics. With the help of an eye-tracker, we evaluated Broker-Insigths usability with ten naive users on the offline fashion and also performed an evaluation in the wild with three insurance brokers. Results achieved show that data mining methods, while combined with interactive data visualization improved the user experience and decision-making process into the recommendation task, and increased the products recommendation acceptance.


Recommender system Visual analytics Data mining Insurance brokerage 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Informatics – Federal University of Rio Grande do Sul (UFRGS)Porto AlegreBrazil

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